#coding: utf-8
__author__ = 'Sail'

from numpy import *

def loadSimpleData():
    dataMat = matrix([[1.,2.1],[2,1.1],[1.3,1.],[1.,1.],[2.,1.]])
    classLabels = [1.0,1.0,-1.0,-1.0,1.0]
    return dataMat, classLabels

def stumpClassify(dataMatrix,dimen,threshVal,threshIneq):
    retArray = ones((shape(dataMatrix)[0],1))
    if threshIneq == 'lt':
        retArray[dataMatrix[:,dimen] <= threshVal] = -1.0
    else:
        retArray[dataMatrix[:,dimen] > threshVal] = -1.0
    return retArray

def buildStump(dataArr, classLabels, D):
    dataMatrix = mat(dataArr)
    labelMat = mat(classLabels).T
    m,n = shape(dataMatrix)
    numSteps = 10.0
    bestStump = {}
    bestClasEst = mat(zeros((m,1)))
    minError = inf
    for i in range(n):
        rangeMin = dataMatrix[:,i].min()
        rangeMax = dataMatrix[:,i].max()
        stepSize = (rangeMax - rangeMin) / numSteps
        for j in range(-1,int(numSteps)+1):
            for inequal in ['lt','gt']:
                threshVal = (rangeMin + float(j) * stepSize)
                predictedVals = stumpClassify(dataMatrix,i,threshVal,inequal)
                errArr = mat(ones((m,1)))
                errArr[predictedVals == labelMat] = 0
                weightedError = D.T * errArr
                print "split: dim %d, thresh %.2f, thresh ineq: %s, the weighted error is %.3f" % \
                      (i,threshVal,inequal,weightedError)
                if weightedError < minError:
                    minError = weightedError
                    bestClasEst = predictedVals.copy()
                    bestStump['dim'] = i
                    bestStump['thresh'] = threshVal
                    bestStump['ineq'] = inequal
    return bestStump,minError,bestClasEst

def adaBoostTrainDS(dataArr, classLabels, numIt = 40):
    weakClassArr = []
    m = shape(dataArr)[0]
    D = mat(ones((m,1))/m)
    aggClassEst = mat(zeros((m,1)))
    for i in range(numIt):
        bestStump, error, classEst = buildStump(dataArr, classLabels, D)
        print "D:", D.T
        alpha = float(0.5 * log((1.0-error)/max(error,1e-16)))
        bestStump['alpha'] = alpha
        weakClassArr.append(bestStump)
        print "classEst: ",classEst.T
        expon = multiply(-1 * alpha * mat(classLabels).T, classEst)
        D = multiply(D, exp(expon))
        D = D / D.sum()
        aggClassEst += alpha * classEst
        print "aggClassEst: ", aggClassEst.T
        aggErrors = multiply(sign(aggClassEst) != mat(classLabels).T, ones((m,1)))
        errorRate = aggErrors.sum() / m
        print "total error: ", errorRate, "\n"
        if errorRate == 0.0:
            break
    return weakClassArr,aggClassEst

def adaClassify(datToClass, classifierArr):
    dataMatrix = mat(datToClass)
    m = shape(dataMatrix)[0]
    aggClassEst = mat(zeros((m,1)))
    for i in range(len(classifierArr)):
        classEst = stumpClassify(dataMatrix,classifierArr[i]['dim'],classifierArr[i]['thresh'],classifierArr[i]['ineq'])
        aggClassEst += classifierArr[i]['alpha'] * classEst
        print aggClassEst
    return sign(aggClassEst)

def loadDataSet(fileName):
    numFeat = len(open(fileName).readline().split('\t'))
    dataMat = []
    labelMat = []
    fr = open(fileName)
    for line in fr.readlines():
        lineArr = []
        curLine = line.strip().split('\t')
        for i in range(numFeat-1):
            lineArr.append(float(curLine[i]))
        dataMat.append(lineArr)
        labelMat.append(float(curLine[-1]))
    return dataMat, labelMat

def plotROC(predStrengths, classLabels):
    import matplotlib.pyplot as plt
    cur = (1.0,1.0) #cursor
    ySum = 0.0 #variable to calculate AUC
    numPosClas = sum(array(classLabels)==1.0)
    yStep = 1/float(numPosClas); xStep = 1/float(len(classLabels)-numPosClas)
    sortedIndicies = predStrengths.argsort()#get sorted index, it's reverse
    fig = plt.figure()
    fig.clf()
    ax = plt.subplot(111)
    #loop through all the values, drawing a line segment at each point
    for index in sortedIndicies.tolist()[0]:
        if classLabels[index] == 1.0:
            delX = 0; delY = yStep;
        else:
            delX = xStep; delY = 0;
            ySum += cur[1]
        #draw line from cur to (cur[0]-delX,cur[1]-delY)
        ax.plot([cur[0],cur[0]-delX],[cur[1],cur[1]-delY], c='b')
        cur = (cur[0]-delX,cur[1]-delY)
    ax.plot([0,1],[0,1],'b--')
    plt.xlabel('False positive rate'); plt.ylabel('True positive rate')
    plt.title('ROC curve for AdaBoost horse colic detection system')
    ax.axis([0,1,0,1])
    plt.show()
    print "the Area Under the Curve is: ",ySum*xStep